Most enterprise AI agent projects fail not because the technology lacks intelligence, but because trust has not been engineered into the system. Trust is not a vague feeling, it is an auditable, measurable state that can be tested, governed and certified.

This post sets out a framework for crossing the “POC Wall”, the moment when pilot projects stall because boards demand guarantees of reliability, compliance, and explainability that the prototype cannot provide. We argue for a new gate in enterprise AI adoption: the Proof of Governance (PoG Gate). To cross it, enterprises must implement the Trust Stack (Evidence, Control, Accountability) and measure progress using a Trust-to-Operate (TTO) Scorecard.

Global standards and regulations reinforce this direction. The NIST AI Risk Management Framework (AI RMF 1.0) was released in January 2023; the NIST Generative AI Profile (NIST-AI-600-1) followed in July 2024, with ~200 specific actions for generative systems. The ISO/IEC 42001:2023 standard created the first AI Management System (AIMS). The EU AI Act entered into force on 1 August 2024, with phased obligations from 2025 to 2027. These frameworks make one point clear: scaling AI agents requires trust as a verifiable asset, not a side note.

The Enterprise hype-reality gap

In 2025, it is hard to find an enterprise board deck that does not reference “autonomous agents”. They are pitched as the Swiss Army knife of efficiency: automated customer service, sales acceleration, operational optimisation. The demos are dazzling, the ROI slides compelling.

Yet the reality is sobering: most initiatives remain in proof-of-concept limbo. Industry surveys show that fewer than one in ten agent pilots progress to scaled production deployments. The reasons cited are not technical immaturity but risk concerns, compliance uncertainty and lack of explainability.

In short, demos compress complexity while production environments expose it. The gap between hype and adoption is not about “smarter models”, it is about creating conditions under which executives can sign off deployment with confidence.

Defining the POC wall

The POC Wall is the point at which enthusiasm meets governance. It is the moment when a CFO or Chief Risk Officer asks: “Who is accountable if this agent makes the wrong decision?” and no satisfactory answer exists.

Symptoms are familiar across industries:

• Sponsors frustrated when promising pilots stall.

• Boards sceptical after a string of failed AI showcases.

• Innovation teams trapped, unable to move forward without guarantees that prototypes cannot provide.

The real transition is not POC → Production, but POC → PoG (Proof of Governance). Only when governance artefacts exist, explanations, guardrails, auditability can agents earn their licence to operate.

The Trust Stack

Instead of listing generic “risks”, enterprises should think of trust as a stack of three layers, each building on the other:

Evidence (Explainability & Lineage)

Without evidence, no assurance is possible. Enterprises must be able to reconstruct why an agent took a decision, inputs, rules, reasoning path, outputs. This is the equivalent of financial audit trails.

Decision lineage, model cards, and structured explanation frameworks are not “nice to have”, they are the foundation of trust.

Control (Executable Guardrails)

Agents often hallucinate, breach rules, or deliver inconsistent outputs. Enterprises need guardrails that are enforceable at runtime: policy allow-lists, deny-lists, automated regression tests.

Think of this as the AI equivalent of access controls and firewalls. Without enforcement, governance remains a theory.

Accountability (Governance & Auditability)

The law does not recognise “the AI did it” as a defence. In 2024, an Air Canada chatbot misled a customer about refund policies, the court ruled the airline was responsible, not the agent.

Accountability is non-negotiable. Enterprises need clear ownership of agent actions, documented delegation levels, and auditable oversight. Responsibility cannot be outsourced to algorithms.

From PoC to PoG Gate (Proof of Governance)

Enterprises should institutionalise a PoG Gate: a mandatory checkpoint between pilot and production. No project advances without satisfying governance criteria.

A robust PoG Gate requires at minimum:

• Decision Register:  a catalogue of agent decisions with rationales.

• Guardrail Pack:  executable policies with test evidence.

• Audit Pack: signed logs, version histories, change notes.

• Risk Mapping:  alignment with NIST AI RMF and ISO/IEC 42001 clauses.

• Reg Readiness:  classification under the AI Act (e.g. high-risk vs GPAI).

This is the missing “gate” that turns proof-of-concept enthusiasm into board-level sign-off. Without it, most projects stall.

Metrics that make trust measurable

Boards do not need more hype, they need numbers. Trust becomes real only when it can be measured. A proposed Trust-to-Operate (TTO) Scorecard includes:

• MTTE (Mean Time To Explanation): how fast can the team produce a verifiable rationale

• Guardrail Coverage (%): percentage of high-risk scenarios covered by executable policies.

• Action Assurance Level (AAL 1–4): codifying the degree of autonomy delegated:

• AAL1 = read-only

• AAL2 = suggest

• AAL3 = supervised execution,

• AAL4 = full autonomous execution.

• Decision Auditability Score: proportion of decisions with full lineage.

• Policy Conformance Rate:  guardrail tests passed in pre-production and production.

• Incident Containment Time:  mean time to detect, resolve and remediate an AI incident.

• Reg Readiness Index: coverage of NIST, ISO/IEC 42001 and AI Act obligations.

These metrics echo the trajectory of cloud and SaaS adoption. Enterprises only scaled once SLAs, uptime guarantees, and certifications (ISO 27001, SOC 2) gave boards quantifiable assurance.

Regulatory & standards compass

• NIST AI RMF 1.0 (2023):  a voluntary framework structured around Govern, Map, Measure, Manage.

• NIST Generative AI Profile (2024): ~200 recommended actions across 12 generative risks.

• ISO/IEC 42001:2023: the first management system standard dedicated to AI.

• EU AI Act: entered into force 1 August 2024. Key dates:

• 2 February 2025: bans on prohibited practices and AI literacy obligations.

• 2 August 2025: governance for GPAI and new AI Office powers.

• 2 August 2026:  main obligations for providers and deployers apply.

• 2 August 2027:  additional obligations for high-risk AI systems.

Rather than treating compliance as a burden, enterprises should use these standards as templates for trust engineering. Mapping internal controls to external frameworks accelerates both board approval and regulatory readiness.

Operating model for decision assurance

Trust is not a one-off exercise, it is an operating model.

Roles (RACI):

• Product: owns decision scope.

• Risk/Legal: defines guardrails.

• Data/ML: manages models and data quality.

• SRE/Operations: ensures observability and reliability.

• Internal Audit: validates assurance.

Runbooks: change management for models/rules, rollback procedures, incident playbooks.

Cadence:

• Value Sprints deliver new capabilities.

• Trust Sprints reinforce evidence, guardrails, and audits.

Speed comes from governance embedded into the build cycle not from bypassing it.

What Enterprises should ask themselves

Before scaling agents, boards should demand clear answers to five questions:

• Explainability: Can we provide a decision rationale within SLA (e.g. MTTE ≤ 24h)?

• Guardrails: What proportion of critical risks are covered by executable policies?

• Auditability: Do we maintain a signed audit trail for every significant decision?

• Accountability: Who approves the agent’s Action Assurance Level?

• Compliance: Are we aligned with ISO/IEC 42001, NIST AI RMF and AI Act timelines?

If the answers are vague, the project is not ready to cross the PoG Gate.

Trust is harder to engineer than code

Enterprise AI agents do not fail for lack of intelligence; they fail for lack of engineered trust.

The frontier is not models that “think harder”. It is a system that enterprises can audit, govern, and delegate authority to at scale.

Trust is harder to engineer than code. But without it, no code will ever reach production.